ArticleScientific data2023
An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- [Construction of a Multi-Factor Quantitative Scoring Model for Chromosome Karyotype Analysis].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Automated karyotyping and structural anomaly detection through a hybrid multi-stage deep learning framework integrating chromosome detection, pairwise classification, and autoencoder-based analysis.Scientific reports · 2026Article
- A comprehensive foundation model for generalizable cytogenetics in precision oncology with CHROMA.NPJ precision oncology · 2026Article
- MetaChrome: an open-source, user-friendly tool for automated metaphase chromosome analysis.Methods (San Diego, Calif.) · 2026Article
- Exploring HSI imagery for overlapped chromosomes segmentation.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2025Article
- ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images.IEEE open journal of engineering in medicine and biology · 2025Article
- ChromTR: chromosome detection in raw metaphase cell images via deformable transformers.Frontiers of medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Chromosomes are a principal target of clinical cytogenetic studies. While chromosomal analysis is an integral part of prenatal care, the conventional manual identification of chromosomes in images is time-consuming and costly. This study developed a chromosome detector that uses deep learning and that achieved an accuracy of 98.88% in chromosomal identification. Specifically, we compiled and made available a large and publicly accessible database containing chromosome images and annotations for training chromosome detectors. The database contains five thousand 24 chromosome class annotations and 2,000 single chromosome annotations. This database also contains examples of chromosome variations. Our database provides a reference for researchers in this field and may help expedite the development of clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.